Feature Selection Methods for ICU Mortality Prediction Model

Abstract

The goal of this research is to offer insightful information that can improve Ethiopia's intensive care unit (ICU) services. There is an increased risk of patients' death in Intensive Care Units (ICUs). This is because of several variables, including preexisting medical issues, lack of resources, and delayed decisions. Healthcare professionals can better prioritize their patients in need of intensive care, distribute resources more efficiently, and enhance patient outcomes by using predictive models to estimate ICU mortality. ICU data is collected from five Ethiopian public hospitals to develop a machine learning method for predicting ICU mortality. The data includes demographic features, vital signs, lab results, and discharge status of 10,798 ICU dataset records. We employed a range of feature selection techniques, such as filters, wrappers, and embedding methods, to identify the most crucial features for mortality prediction. We also compared the performance of two machine learning algorithms, Random Forest and Decision Tree. These models are trained using ICU data with features encompassing age, length of stay, temperature, neutrophil, Diagnosis (DX) condition, PH, and Lymphocite. These features are selected using Recursive Feature Elimination (RFE). Using a number of different evaluation methods, including accuracy (99.7%), precision (99.4%), recall (98.8%), F1 score (99.1%), and area under the curve (AUC) (99.3%), Random Forest performed better than Decision Tree. Based on our findings, we made recommendations for healthcare practitioners and policy makers. We also suggest key future research directions for researchers in the area.